1. Field of Invention
This invention is in the field of autofocus methods for Synthetic Aperture Radar (SAR) imaging.
2. Description of the Related Art
Synthetic Aperture Radar (SAR) radar is used for ground mapping as well as target identification. The general principle behind SAR is to coherently combine the amplitude and phase information of radar returns from a plurality of sequentially transmitted pulses. These pulses are from a relatively small antenna on a moving platform. As the platform moves, the information contained in the pulses is combined to arrive at a high resolution SAR image.
The plurality of returns creating a SAR image generated by the transmitted pulses along a presumed known path of the platform make up a frame length. Theoretically, during the frame length, amplitude as well as phase information returned from each of the pulses, for each of many range bins, is preserved. The SAR image is formed from the coherent combination of the amplitude and phase of return(s) within each range bin, motion compensated for spatial displacement of the moving platform during the acquisition of the returns for the duration of the frame length.
The plurality of pulses transmitted during an SAR frame length, when coherently combined and processed, result in image quality comparable to a longer antenna, corresponding approximately to the “length” traveled by the antenna during the frame length. The clarity of a SAR image is in many respects dependent on the quality of the motion compensation applied to each radar return prior to SAR image computation. Motion compensation shifts the phase of each radar sample (typically an I+jQ complex quantity derived from an analog to digital converter) in accordance with the motion in space of the moving platform. The SAR imaging process depends on the coherent, phase accurate summing of all radar returns expected within a frame.
For certain applications the accuracy of the motion compensation applied to each radar A/D sample is insufficient. For more accurate phase alignment accuracy autofocus methods are used. Autofocus methods typically use radar returns of a SAR image itself in an attempt to phase align radar return samples to accuracies better than those available from motion compensation alone.
The Map Drift Method is an example of an autofocus method of the prior art and is described by C. E. Mancil and J. M. Swiger in A Map Drift Autofocus technique for Correlating High Order SAR Phase Errors(U) 27th Annual Tri Service Radar Symposium Record, Monterey, Calif. June 1981 pp 391–400. Here, the estimation of quadratic error terms, the errors typically computed using autofocus techniques, is arrived at by dividing the aperture in the spatial frequency domain into two sub-apertures. An estimate of the relative shift of the two maps is computed to arrive at the quadratic error terms. Higher order terms can be computed by dividing the aperture into sub-apertures of smaller size and estimating relative image shifts for each sub-aperture. However, as the order of the phase error increases, and the sub-apertures get smaller, the estimated phase error tends to lose accuracy because of the reduced Signal to Noise Ratio (SNR). Because of this fundamental limitation, the Map Drift Method is limited to the case of low order corrections.
For high order phase corrections, another example of the prior art is applied. This is the Phase Gradient Autofocus (PGA) method, as described by D. E. Wahl, et al, Phase Gradient Autofocus—A Robust Tool for High Resolution SAR Phase correction, IEEE Transactions on Aerospace Electronic Systems, vol 30, pp 827–834, March 1994. The PGA method is based on the estimation of differential phase error of isolated point targets. Estimation accuracy is improved by averaging estimates from multiple point like targets. Because of the requirement of point like targets, PGA fails where there are no point like targets to be used with this method.
Both Map Drift and PGA methods lack metrics to determine the quality of the phase error estimates computed by each method.
Yet another approach in the prior art is based on SAR image quality. Because image quality improvement through phase error correction is related to the improvement in contrast or sharpness of the SAR image, optimized phase error estimates can be computed from improvements in the contrast/sharpness of the SAR image. An example of this iterative approach is described by L. Xi, L Gousui and J. Ni in Autofocusing ISAR Images based on Entropy Minimization IEEE Transactions on Aerospace Electronic Systems, vol 35, pp 1240–1252, October 1999. This autofocus technique, called stage by stage approaching algorithm (SSA) is based on entropy minimization and can provide an estimation of very high order phase errors. However, the computational requirements of SSA is prohibitively intense for SAR data, thus impractical with current airborne computer technology.